




import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  assert isinstance(__lowerCAmelCase   , __lowerCAmelCase				)
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory'''   , [False, True]				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      tmp_path / '''cache'''
  lowerCAmelCase__										=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowerCAmelCase__										=      ParquetDatasetReader(__lowerCAmelCase   , cache_dir=__lowerCAmelCase   , keep_in_memory=__lowerCAmelCase				).read()
  _check_parquet_dataset(__lowerCAmelCase   , __lowerCAmelCase				)
@pytest.mark.parametrize(
    '''features'''   , [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]   , )
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      tmp_path / '''cache'''
  lowerCAmelCase__										=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowerCAmelCase__										=      features.copy() if features else default_expected_features
  lowerCAmelCase__										=      (
      Features({feature: Value(__lowerCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
  )
  lowerCAmelCase__										=      ParquetDatasetReader(__lowerCAmelCase   , features=__lowerCAmelCase   , cache_dir=__lowerCAmelCase				).read()
  _check_parquet_dataset(__lowerCAmelCase   , __lowerCAmelCase				)
@pytest.mark.parametrize('''split'''   , [None, NamedSplit('''train'''				), '''train''', '''test''']				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      tmp_path / '''cache'''
  lowerCAmelCase__										=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowerCAmelCase__										=      ParquetDatasetReader(__lowerCAmelCase   , cache_dir=__lowerCAmelCase   , split=__lowerCAmelCase				).read()
  _check_parquet_dataset(__lowerCAmelCase   , __lowerCAmelCase				)
  assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type'''   , [str, list]				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):




  if issubclass(__lowerCAmelCase   , __lowerCAmelCase				):
    lowerCAmelCase__										=      parquet_path
  elif issubclass(__lowerCAmelCase   , __lowerCAmelCase				):
    lowerCAmelCase__										=      [parquet_path]
  lowerCAmelCase__										=      tmp_path / '''cache'''
  lowerCAmelCase__										=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowerCAmelCase__										=      ParquetDatasetReader(__lowerCAmelCase   , cache_dir=__lowerCAmelCase				).read()
  _check_parquet_dataset(__lowerCAmelCase   , __lowerCAmelCase				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase=("train",)				):
  assert isinstance(__lowerCAmelCase   , __lowerCAmelCase				)
  for split in splits:
    lowerCAmelCase__										=      dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory'''   , [False, True]				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      tmp_path / '''cache'''
  lowerCAmelCase__										=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowerCAmelCase__										=      ParquetDatasetReader(
        {'''train''': parquet_path}   , cache_dir=__lowerCAmelCase   , keep_in_memory=__lowerCAmelCase				).read()
  _check_parquet_datasetdict(__lowerCAmelCase   , __lowerCAmelCase				)
@pytest.mark.parametrize(
    '''features'''   , [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]   , )
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      tmp_path / '''cache'''
  lowerCAmelCase__										=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowerCAmelCase__										=      features.copy() if features else default_expected_features
  lowerCAmelCase__										=      (
      Features({feature: Value(__lowerCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
  )
  lowerCAmelCase__										=      ParquetDatasetReader({'''train''': parquet_path}   , features=__lowerCAmelCase   , cache_dir=__lowerCAmelCase				).read()
  _check_parquet_datasetdict(__lowerCAmelCase   , __lowerCAmelCase				)
@pytest.mark.parametrize('''split'''   , [None, NamedSplit('''train'''				), '''train''', '''test''']				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):




  if split:
    lowerCAmelCase__										=      {split: parquet_path}
  else:
    lowerCAmelCase__										=      '''train'''
    lowerCAmelCase__										=      {'''train''': parquet_path, '''test''': parquet_path}
  lowerCAmelCase__										=      tmp_path / '''cache'''
  lowerCAmelCase__										=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowerCAmelCase__										=      ParquetDatasetReader(__lowerCAmelCase   , cache_dir=__lowerCAmelCase				).read()
  _check_parquet_datasetdict(__lowerCAmelCase   , __lowerCAmelCase   , splits=list(path.keys()				)				)
  assert all(dataset[split].split == split for split in path.keys()				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      ParquetDatasetWriter(__lowerCAmelCase   , tmp_path / '''foo.parquet'''				)
  assert writer.write() > 0
  lowerCAmelCase__										=      pq.ParquetFile(tmp_path / '''foo.parquet'''				)
  lowerCAmelCase__										=      pf.read()
  assert dataset.data.table == output_table
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      str(shared_datadir / '''test_image_rgb.jpg'''				)
  lowerCAmelCase__										=      {'''image''': [image_path]}
  lowerCAmelCase__										=      Features({'''image''': Image()}				)
  lowerCAmelCase__										=      Dataset.from_dict(__lowerCAmelCase   , features=__lowerCAmelCase				)
  lowerCAmelCase__										=      ParquetDatasetWriter(__lowerCAmelCase   , tmp_path / '''foo.parquet'''				)
  assert writer.write() > 0

  lowerCAmelCase__										=      Dataset.from_parquet(str(tmp_path / '''foo.parquet'''				)				)
  assert dataset.features == reloaded_dataset.features

  lowerCAmelCase__										=      ParquetDatasetReader(str(tmp_path / '''foo.parquet'''				)   , streaming=__lowerCAmelCase				).read()
  assert dataset.features == reloaded_iterable_dataset.features




@pytest.mark.parametrize(
    '''feature, expected'''   , [
        (Features({'''foo''': Value('''int32'''				)}				), None),
        (Features({'''image''': Image(), '''foo''': Value('''int32'''				)}				), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'''nested''': Sequence(Audio()				)}				), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]   , )
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  assert get_writer_batch_size(__lowerCAmelCase				) == expected








"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      lowerCAmelCase    		=			list(s_dict.keys()     )
      for key in keys:
            if "transformer_layers" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )
            elif "subsample" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any     ):







      '''simple docstring'''




      lowerCAmelCase		,						lowerCAmelCase    		=			emb.weight.shape
      lowerCAmelCase    		=			nn.Linear(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		bias=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			emb.weight.data
      return lin_layer


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




      lowerCAmelCase    		=			torch.load(SCREAMING_SNAKE_CASE				,		map_location="""cpu"""     )
      lowerCAmelCase    		=			mam_aaa["""args"""]
      lowerCAmelCase    		=			mam_aaa["""model"""]
      lowerCAmelCase    		=			state_dict["""decoder.output_projection.weight"""]

      remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
      rename_keys(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			state_dict["""decoder.embed_tokens.weight"""].shape[0]

      lowerCAmelCase    		=			args.share_decoder_input_output_embed

      lowerCAmelCase    		=			[int(SCREAMING_SNAKE_CASE     ) for i in args.conv_kernel_sizes.split(""","""     )]
      lowerCAmelCase    		=			SpeechaTextConfig(
          vocab_size=SCREAMING_SNAKE_CASE				,		max_source_positions=args.max_source_positions				,		max_target_positions=args.max_target_positions				,		encoder_layers=args.encoder_layers				,		decoder_layers=args.decoder_layers				,		encoder_attention_heads=args.encoder_attention_heads				,		decoder_attention_heads=args.decoder_attention_heads				,		encoder_ffn_dim=args.encoder_ffn_embed_dim				,		decoder_ffn_dim=args.decoder_ffn_embed_dim				,		d_model=args.encoder_embed_dim				,		dropout=args.dropout				,		attention_dropout=args.attention_dropout				,		activation_dropout=args.activation_dropout				,		activation_function="""relu"""				,		num_conv_layers=len(SCREAMING_SNAKE_CASE     )				,		conv_channels=args.conv_channels				,		conv_kernel_sizes=SCREAMING_SNAKE_CASE				,		input_feat_per_channel=args.input_feat_per_channel				,		input_channels=args.input_channels				,		tie_word_embeddings=SCREAMING_SNAKE_CASE				,		num_beams=5				,		max_length=2_00				,		use_cache=SCREAMING_SNAKE_CASE				,		decoder_start_token_id=2				,		early_stopping=SCREAMING_SNAKE_CASE				,		)

      lowerCAmelCase    		=			SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase		,						lowerCAmelCase    		=			model.model.load_state_dict(SCREAMING_SNAKE_CASE				,		strict=SCREAMING_SNAKE_CASE     )
      if len(SCREAMING_SNAKE_CASE     ) > 0 and not set(SCREAMING_SNAKE_CASE     ) <= {
          "encoder.embed_positions.weights",
          "decoder.embed_positions.weights",
      }:
            raise ValueError(
                """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
                F' but all the following weights are missing {missing}'     )

      if tie_embeds:
            lowerCAmelCase    		=			make_linear_from_emb(model.model.decoder.embed_tokens     )
      else:
            lowerCAmelCase    		=			lm_head_weights

      model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)






